SEP 789 / Deep Learning and Its Applications

1.5 unit(s)

Cross-listed as CHEM ENG 789 and MECH ENG 789

Prerequisite(s): Working knowledge of one of the programming languages (Python, C++, C#, MATLAB).
Must be enrolled in SEPT. However, permission will be granted to other students from other graduate programs with department consent.

Motivation and Paradigm. Convolutional Neural Network (CNN) architecture. Training deep CNNs. Image classification with deep CNNs. Recurrent Neural Network (RNN) architecture and training. RNN Extensions: Deep RNNs, Bidirectional RNNs, Long Short Term Memory (LSTM) networks. RNN Applications in machine translation, language modelling, joint language and translation modelling. Autoencoders. Deep Residual Networks. Generative Adversarial Networks (GANs). Real-world use cases.